Joellen M. Schildkraut

The paper develops a functional data analysis framework to model spatial clustering in ovarian cancer, identifying prognostic T cell patterns beyond immune cell abundance.

The paper shows that bulk transcriptomic subtypes in HGSC reflect tumor composition rather than intrinsic biology, with composition predicting subtype and prognosis.

The paper deconvolves adipocyte proportions from bulk RNA-seq data of HGSOC tumors and finds higher adipocyte content is associated with worse survival.

This study identifies molecular subtypes of high-grade serous ovarian cancer across racial groups and gene expression platforms, finding similar subtype-specific survival rates despite differences in subtype prevalence.

This study performs cis- and trans-eQTL TWAS on breast and ovarian cancer data to identify 101 and 8 risk-associated genes, respectively, with several driven by trans-eQTLs.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

The paper identifies RCCD1 as a susceptibility gene for breast and ovarian cancer by fine-mapping a risk locus and showing its expression and interaction patterns are associated with cancer risk.

This study assesses polygenicity and risk prediction using common variants across 14 cancers, finding varying numbers of susceptibility variants and risk score effectiveness.

This study combines genome-wide association data from four cancers to identify shared and novel genetic associations and pleiotropic effects.